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Found 24 papers, 7 papers with code
Turbo2K: Towards Ultra-Efficient and High-Quality 2K Video Synthesis
Compared to existing methods, Turbo2K is up to 20$\times$ faster for inference, making high-resolution video generation more scalable and practical for real-world applications.
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy.
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Beyond Hamiltonian prediction, the proposed sparsification techniques also hold significant potential for improving the efficiency and scalability of other SE(3) equivariant networks, further broadening their applicability and impact.
MagicEraser: Erasing Any Objects via Semantics-Aware Control
However, the object erasure task, which is in increasing demand, aims to erase objects and generate harmonious background.
Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry.
UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks
Ultra-high-resolution image generation poses great challenges, such as increased semantic planning complexity and detail synthesis difficulties, alongside substantial training resource demands.
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Models (DEQs) for predicting Density Functional Theory (DFT) Hamiltonians.
SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
In this paper, we develop SE3Set, an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning.
F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
Molecular dynamics (MD) is a crucial technique for simulating biological systems, enabling the exploration of their dynamic nature and fostering an understanding of their functions and properties.
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems.
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research.
CLIPPING: Distilling CLIP-Based Models With a Student Base for Video-Language Retrieval
Pre-training a vison-language model and then fine-tuning it on downstream tasks have become a popular paradigm.
HiVLP: Hierarchical Interactive Video-Language Pre-Training
However, compared to image-language pre-training, VLP has lagged far behind due to the lack of large amounts of video-text pairs.
PIDRo: Parallel Isomeric Attention with Dynamic Routing for Text-Video Retrieval
The parallel isomeric attention module is used as the video encoder, which consists of two parallel branches modeling the spatial-temporal information of videos from both patch and frame levels.
Ranked #3 on
Video Retrieval
on MSR-VTT-1kA
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task.
Multi-View Substructure Learning for Drug-Drug Interaction Prediction
Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment.
Co-evolution Transformer for Protein Contact Prediction
These methods generally derive coevolutionary features by aggregating the learned residue representations from individual sequences with equal weights, which is inconsistent with the premise that residue co-evolutions are a reflection of collective covariation patterns of numerous homologous proteins.
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences.
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
In this paper, we propose a framework to construct SE(3) equivariant graph neural networks that can approximate the geometric quantities efficiently.
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.
Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation
We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs).
Defending Model Inversion and Membership Inference Attacks via Prediction Purification
Neural networks are susceptible to data inference attacks such as the model inversion attack and the membership inference attack, where the attacker could infer the reconstruction and the membership of a data sample from the confidence scores predicted by the target classifier.
SEEK: Segmented Embedding of Knowledge Graphs
In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering.
Ranked #1 on
Link Prediction
on YAGO37
Hallucinating very low-resolution and obscured face images
The inpainting network restores the low-resolution(LR) obscured face images.
